Simulation mathematically models and numerically calculates in a computer a phenomenon taking place in the real world and a hypothetical situation. Mathematical modelling enables calculation to be performed with time and space set as desired. Such simulation enables a situation in which obtaining actual results is difficult (for example, a situation at a place where performing observation is difficult) and an event that may occur in the future to be predicted. Varying conditions for calculation intentionally enables features and behavior in a situation that is difficult to actually observe to be studied. Such a simulation result may be put to use as an indicator in theoretical clarification of a cause-and-effect relationship, designing, planning, or the like.
In particular, simulation is effective in the case in which it is desired to grasp and understand a state continuously over a wide range in a situation in which observation data actually obtained have a small number of data pieces and a distribution biased not only spatially but also temporally. However, simulation is only imitating a reality mathematically, and accuracy thereof thus depends on how deeply the reality is understood and how faithfully imitated. Therefore, in the case of targeting a phenomenon whose actual observation data is small in quantity as described above and which is incompletely understood, a model comes to include incompleteness. Moreover, since calculation is performed discretely, fractionation of a target domain and a large amount of calculation are required to grasp it continuously over a wide range. In practice, however, there is no other choice but to set a calculation condition including incompleteness in accordance with allowable computation time and computational resources. Such incompleteness reduces the accuracy of simulation.
Thus, as a scheme for improving the accuracy of simulation under the condition having such incompleteness, data assimilation is known. Data assimilation is a method of incorporating observation data obtained from reality into a numerical simulation. Even performing simulation based on the same mathematical model yields various results depending on the afore-described internal incompleteness, a given initial condition, a boundary condition, and the like. Data assimilation searches out a result explaining observation data obtained in reality best from the various simulation results and, at the same time, updates the model and conditions.
Data assimilation, which is often used in earth science and oceanography, not only has been developed especially in meteorology and has been contributing to improvements in accuracy of daily weather forecasts, but also new methods thereof have been proposed successively. That is partly because improvements in observation technologies relating to weather have caused obtainable observation data to become diverse and the quantity of data has increased not only spatially but also temporally.
In weather forecasting, improving spatial and temporal accuracy of simulation to predict a rapidly developing thundercloud and rain accurately is also a problem. As a related technology for coping with such a problem, a weather prediction device disclosed in PTL 1 is known. The weather prediction device uses precipitable water data collected by GPS receivers, which are placed at a lot of locations and which enable frequent observation, wind direction and wind speed data collected by Doppler radars, and rainfall intensity data collected by Radar-AMeDAS. The weather prediction device takes in the above-described data measured in real time or quasi-real time and performs data assimilation using the three-dimensional variational method. As another related technology for coping with the problem described above, a synchronization device and a meshing device disclosed in PTL 2 is known. When data pieces from a plurality of observation devices are asynchronous, the synchronization device reorganizes the observation data pieces on the time axis by means of interpolation processing to synchronize the observation data pieces so that the observation data pieces indicate observation data pieces of the same time. The meshing device rearranges, in a target domain, synchronized observation data pieces collected at a plurality of places so that the observation data pieces are positioned at mesh points (grid points) with a fixed distance interval in a horizontal space.
After a natural disaster, an artificial accident, and the like caused by a sudden change in weather or a marine environment, grasping a state of the soil accurately over a wide range is required. To achieve the requirement, observing and estimating a state of the soil by use of a wide-ranging observation means and with high accuracy becomes a problem. As a related technology for coping with such a problem, a method disclosed in PTL 3 is known. Although not being a method utilizing simulation, the method, using satellite images collected at three or more times, estimates feature quantities representing corresponding states of the soil. For more details, when there is not enough time and cost for conducting observation and investigation in the field or it is dangerous to approach the field, the method uses image data collected by a synthetic aperture radar (SAR) or the like mounted on an artificial satellite. The method, using the satellite SAR images, enables a state of the soil to be grasped speedily, over a wide range, and safely. As a specific example, an example in which soil salinity and drainability in a coastal area after a tsunami occurrence are estimated on the basis of changes in index values of soil moisture content recorded in satellite images collected at three times: before the tsunami occurrence; immediately thereafter; and several months later is described in PTL 3.
An example of a method of, by use of satellite SAR images and a yield prediction model of a crop, performing yield prediction of paddy rice fields in wide-ranging areas in the first half of a growing period with little labor and with high accuracy is described in PTL 4. In general, an optical sensor mounted on a satellite that observes the intensity of reflected light from sunlight in the visible and near-infrared regions is substantially influenced from weather, as in the case in which, when there is a cloud, observation is not able to be performed. Thus, in the method, SAR image data collected using a microwave (X-band: wavelength of 3.1 cm), which is not influenced by a cloud, are used. In the method, a yield prediction expression, using regression analysis, based on a correlation between obtained SAR image data and a quantity representing the growth state of a crop, such as plant height and the number of stems, is calculated to perform a yield prediction.
Although being different from simulation of the real world, there is a case in which a mathematical model is used for analysis of observation data. For example, in a use of determining whether or not an object exists in a predetermined area using millimeter wave radar or the like, accurate determination based on only observation data was difficult. That is because, in particular, when an object is a walker, reflection intensity of radar is substantially small (an SN ratio, that is, a signal to noise ratio, is small) and, due to various postural change of the walker, reflection intensity changes moment by moment. As a related technology for coping with such a problem, a method disclosed in PTL 5 is known. In the method, from a distribution of reflection intensity with respect to detection positions of an object, feature quantity models of a walker signal and a noise signal are created in advance. By comparing actual observation data with the models, in the method, states including “existent”, “non-existent”, and “unclear” are probabilistically estimated based on non-ideal observation data. In addition, in PTL 5, a method for, with respect to the states of “existence”, “non-existence”, and “unclear”, unifying probabilities of a plurality of states obtained from a plurality of sensors is also described.